mlsanityData to 14 Oct 2026

Visual Closed-Loop Control for Pouring Liquids

arXiv 1610.02610 ↗First posted 9 Oct 2016cs.RO cs.CVcited by 26

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Abstract (arXiv)

Pouring a specific amount of liquid is a challenging task. In this paper we develop methods for robots to use visual feedback to perform closed-loop control for pouring liquids. We propose both a model-based and a model-free method utilizing deep learning for estimating the volume of liquid in a container. Our results show that the model-free method is better able to estimate the volume. We combine this with a simple PID controller to pour specific amounts of liquid, and show that the robot is able to achieve an average 38ml deviation from the target amount. To our knowledge, this is the first use of raw visual feedback to pour liquids in robotics.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Oct 2016.

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Citing papers per year: 2020: 1; 2021: 7; 2022: 5; 2023: 5; 2024: 6; 2025: 2.

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  1. 2021PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-trainingcited by 302
  2. 2020COG: Connecting New Skills to Past Experience with Offline Reinforcement Learningcited by 83
  3. 2023Preference Transformer: Modeling Human Preferences using Transformers for RLcited by 79
  4. 2021A Workflow for Offline Model-Free Robotic Reinforcement Learningcited by 69
  5. 2023Inverse Preference Learning: Preference-based RL without a Reward Functioncited by 68
  6. 2023DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physicscited by 23
  7. 2024Continuously Improving Mobile Manipulation with Autonomous Real-World RLcited by 22
  8. 2024Privileged Sensing Scaffolds Reinforcement Learningcited by 19
  9. 2021Explainable Hierarchical Imitation Learning for Robotic Drink Pouringcited by 11
  10. 2022Self-supervised Transparent Liquid Segmentation for Robotic Pouringcited by 10
  11. 2024Efficient Preference-based Reinforcement Learning via Aligned Experience Estimationcited by 9
  12. 2021Manipulation of Granular Materials by Learning Particle Interactionscited by 7
14 more
  1. 2022One-Shot Domain-Adaptive Imitation Learning via Progressive Learningcited by 6
  2. 2023DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physicscited by 6
  3. 2021Improving filling level classification with adversarial trainingcited by 5
  4. 2024The Sound of Water: Inferring Physical Properties from Pouring Liquidscited by 5
  5. 2021Image Based Reconstruction of Liquids from 2D Surface Detectionscited by 4
  6. 2024Vision-based robot manipulation of transparent liquid containers in a laboratory settingcited by 2
  7. 2021Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrationscited by 1
  8. 2022Visual-Tactile Sensing for Real-time Liquid Volume Estimation in Graspingcited by 1
  9. 2022Data augmentation with mixtures of max-entropy transformations for filling-level classificationcited by 1
  10. 2022Training Robots to Evaluate Robots: Example-Based Interactive Reward Functions for Policy Learningcited by 1
  11. 2023Fluid Viscosity Prediction Leveraging Computer Vision and Robot Interactioncited by 1
  12. 2024Trajectory-Oriented Policy Optimization with Sparse Rewardscited by 1
  13. 2025SCOOP'D: Learning Mixed-Liquid-Solid Scooping via Sim2Real Generative Policycited by 1
  14. 2025ViscNet: Vision-Based In-line Viscometry for Fluid Mixing Processcited by 0